class_docstr: Creates a Dataset for T5/BART architecture based Generative QA 
class_docstr_tok: ["Creates","a","Dataset","for","T5\/BART","architecture","based","Generative","QA"]
docstr: Creates cache filename using dataset config parameters 
docstr_tok: ["Creates","cache","filename","using","dataset","config","parameters"]
code: def _set_cached_features_filename(self):
      """Creates cache filename using dataset config parameters"""
  
      vocab_size = getattr(self.tokenizer, "vocab_size", 0)
      self.cached_features_file = (
          self.data_file
          + "_cache"
          + "_{}_{}_{}_{}_{}_{}_{}".format(
              self.mode,
              self.tokenizer.name,
              str(vocab_size),
              str(self.max_query_length),
              str(self.max_seq_length),
              str(self.max_answer_length),
              str(self.num_samples),
          )
      )
  
file_path: NeMo/nemo/collections/nlp/data/question_answering/dataset/qa_s2s_dataset.py
imports: import os
  import numpy
  import torch
  from tqdm import trange
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import INFERENCE_MODE, TRAINING_MODE
  from nemo.collections.nlp.data.question_answering.dataset.qa_dataset import QADataset
  from nemo.collections.nlp.data.question_answering.input_example.qa_s2s_input_example import S2SQAInputExample
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the S2SQADataset obj's underlying member method code with example usage: obj._set_cached_features_filename() without return types